text: from .model.dcgan_model import DCGANBatchNorm
  from tensorflow.examples.tutorials.mnist import input_data
  import os
  
  os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # suppress tensorflow warnings
  
  # load data
  mnist = input_data.read_data_sets("./data/MNIST_data", one_hot=True)
  
  # create model
  image_dim = 784  # 28*28 pixels * 1 channel
  noise_dim = 100  # Noise data points
  batch_size = 128
  dcgan_batch_model = DCGANBatchNorm(image_dim, noise_dim, batch_size=batch_size)
  
  # training
  # sess = dcgan_batch_model.train(mnist, num_steps=10000, sess=None)
  
  # restore
  sess = dcgan_batch_model.restore_last_session()
  
  # generate
  dcgan_batch_model.generate(sess)
  